Tiered storage for data processing

ABSTRACT

Tiered storage may be implemented for processing data. Data processors may maintain some of a data set, including user data and metadata describing the user data, locally. The data set is also maintained a data store remote to the data processor. When processing requests are received, a determination is made as to whether the local portions of the data set can execute the processing request or one or more additional portions of the data set are needed from the remote data store. If additional portions of the data set are needed, then a request may be sent to the data store for the additional portions. Once received, the data processor may execute the processing request utilizing the additional portions. Portions of the data set maintained locally at the data processor may be selected and flushed from local storage to the remote data store.

This application is a continuation of U.S. patent application Ser. No.15/250,641, filed Aug. 29, 2016, which is hereby incorporated byreference herein in its entirety.

BACKGROUND

As the technological capacity for organizations to create, track, andretain information continues to grow, a variety of differenttechnologies for managing and storing the rising tide of informationhave been developed. Database systems, for example, provide clients withmany different specialized or customized configurations of hardware andsoftware to manage stored information. However, the increasing amountsof data that organizations must store and manage often correspondinglyincreases both the size and complexity of data storage and managementtechnologies, like database systems, which in turn escalate the cost ofmaintaining the information. New technologies more and more seek toreduce both the complexity and storage requirements of maintaining datawhile simultaneously improving the efficiency of data processing.

For example, data processing often relies upon locality to improveprocessing performance. Related data, or other data likely to beaccessed together, are often co-located so that when the data isaccessed for processing, a smaller number input/output (I/O) operationsare performed. Because there are multiple ways data can be related toone another, it can be difficult to select a storage scheme that placesdata together for an optimal number of processing patterns that can beimproved by locality. Moreover, as the size of data sets being processedincreases, it can become challenging to procure enough storage space tolocate data together as desired. Therefore, techniques that improve theability to locate data together may result in improvements to theperformance of data processing.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a logical block diagram of tiered storage for dataprocessing, according to some embodiments.

FIG. 2 is a block diagram illustrating a provider network offering dataprocessing services that utilize other data storage services for tieredstorage, according to some embodiments.

FIG. 3 is a block diagram of a data warehouse service that processesdata utilizing another data storage service for tiered storage,according to some embodiments.

FIG. 4 is a block diagram illustrating an example processing cluster fora data warehouse service, according to some embodiments.

FIG. 5 is a block diagram illustrating an example compute node thatexecutes processing requests for data in tiered storage, according tosome embodiments.

FIG. 6 is a block diagram illustrating an example execution engine thataccesses local and remote data storage to execute processing requests,according to some embodiments.

FIG. 7 is a block diagram illustrating local data management thatmaintains local data storage for executing processing requests,according to some embodiments.

FIG. 8 is a high-level flowchart illustrating methods and techniques toexecute processing requests accessing tiered storage, according to someembodiments.

FIG. 9 is a high-level flowchart illustrating methods and techniques toflush portions of a data set locally maintained at a data processor to aremote data store, according to some embodiments.

FIG. 10 illustrates an example system configured to implement thevarious methods, techniques, and systems described herein, according tosome embodiments.

While embodiments are described herein by way of example for severalembodiments and illustrative drawings, those skilled in the art willrecognize that embodiments are not limited to the embodiments ordrawings described. It should be understood, that the drawings anddetailed description thereto are not intended to limit embodiments tothe particular form disclosed, but on the contrary, the intention is tocover all modifications, equivalents and alternatives falling within thespirit and scope as defined by the appended claims. The headings usedherein are for organizational purposes only and are not meant to be usedto limit the scope of the description or the claims. As used throughoutthis application, the word “may” is used in a permissive sense (i.e.,meaning having the potential to), rather than the mandatory sense (i.e.,meaning must). Similarly, the words “include,” “including,” and“includes” mean including, but not limited to.

It will also be understood that, although the terms first, second, etc.may be used herein to describe various elements, these elements shouldnot be limited by these terms. These terms are only used to distinguishone element from another. For example, a first contact could be termed asecond contact, and, similarly, a second contact could be termed a firstcontact, without departing from the scope of the present invention. Thefirst contact and the second contact are both contacts, but they are notthe same contact.

DETAILED DESCRIPTION OF EMBODIMENTS

Various embodiments of tiered storage for data processing are describedherein. Often data processing is tightly coupled with data storage inorder to ensure that the time to access data in order to performprocessing requests is minimized. However, some data sets are verylarge, requiring a large amount of storage capacity just to maintain thedata set. In order to ensure that processing is tightly coupled, acorrespondingly large number of processing components may then becreated to access and execute upon the storage capacity. For instance, acommon data storage technique creates a cluster of nodes, systems ordevices that provide both storage and processing capacity. The clustermay work in parallel to divide the workload of data processing amongstthe nodes. In such a scheme, the greater the size of the data set, thegreater the size of the cluster. For large data sets, the cluster sizecan become large, resulting in scenarios where only a portion of theprocessing components are utilized if only a portion of the data isprocessed (with remaining processing components underutilized,increasing the cost to maintain the data set). Moreover, locating datathat is commonly accessed together can be challenging as the data may bedivided amongst a large number of nodes.

Implementing tiered storage for data processing, reduces the amount ofstorage capacity that is tightly coupled to data processing resources byonly storing a portion of a data set locally at a data processor (e.g.,storing only portions of a data set at nodes in a cluster). Lessfrequently accessed data may be stored remotely and retrieved whenneeded (e.g., upon demand). Additionally, frequently accessed data canbe located together at a data processor (e.g., at a same node in acluster) to reduce I/O operations when processing the data because thedemands upon storage capacity at the data processor may be reduced (asless frequently accessed data does not also have to be accommodated).Tiered storage may also allow for the portions of data maintainedlocally at a data processor to change over time, adapting to changes inaccess or utilization.

FIG. 1 illustrates a logical block diagram of tiered storage for dataprocessing, according to some embodiments. Various client(s) 100 mayimplement data processor 110 for executing various processing requests102 upon a data set 130. For instance, data set 130 may be a databasetable or structured data object that is searched, analyzed, updated, orotherwise read from in order to obtain various results 104. Processingrequests 102 may, for example, by a query formatted according tostructured query language (SQL), which may specify various querypredicates, conditions, or other operations to return or update a subsetof data satisfying the query predicates, or processing requests may berequests that trigger the execution of previously defined processingjobs (e.g., batch process data stored as part of a previous night'supload). Generally, processing requests may result in reading, writing,obtaining, modifying, or otherwise accessing portions of a data set.

Data processor 110 may utilize tiered storage for data set 130. A remotedata store, data store 120, with respect to data processor 110 maymaintain the entirety of data set 130. For example, data store 120 maymaintain the entire database table or structured data object upon whichprocessing requests 102 are executed. Data processor 110 may implementsome local storage resources (e.g., attached block-based storagedevices) to locally and persistently maintain portions 132 of data set130. When a processing request is received, a determination may be madeas to whether the locally maintained portion(s) 132 are sufficient toexecute the processing request. If not then, as discussed below withregard to FIG. 8, a request for portion(s) 112 may be made to data store120, which may return portion(s) 114. Data processor 110 may thenmaintain the requested portions 114 as part of locally maintainedportions 132 for executing the processing request. In this way, portionsof the data set that are frequently accessed together may be retrievedand maintained locally at data processor 110 without any clientspecification or indication as to how portions of data set 130 may beoptimally stored. Instead, portions of the data set frequently accessedmay be automatically obtained and maintained locally data processor 110,improving the execution of processing requests.

In order to prevent the local storage of data processor 110 from beingfilled, techniques to flush or evict some of locally maintained portions132 from data processor 110 and store or update the correspondingportions 116 in data set 130 at data store 120 may be implemented. Forexample, FIG. 9, discussed in detail below, tracks access statistics forthe different portions of the data, weights them according to a decayfunction, and then selects portions to flush based on the weightedstatistics. Note that various other techniques may be implemented, suchas the least recently used portion of the data set, a first in first outportion selection, random selection, application of various statisticalanalysis (e.g., machine learning) to predictively flush pages that areetc.

Please note that the previous description of tiered storage for dataprocessing is a logical illustration and thus is not to be construed aslimiting as to the implementation of a data processor, a data store, adata set (or portions thereof). For example, data processor 110 may beimplemented as a cluster or group of nodes that perform data processing.

This specification begins with a general description of a providernetwork that implements data processing and/or storage services thatutilized tiered storage to perform data processing. Then variousexamples of a data processor, such as a data warehouse service,including different components/modules, or arrangements ofcomponents/module that may be employed as part of implementing the dataprocessor are discussed. A number of different methods and techniques toimplement tiered storage for data processing are then discussed, some ofwhich are illustrated in accompanying flowcharts. Finally, a descriptionof an example computing system upon which the various components,modules, systems, devices, and/or nodes may be implemented is provided.Various examples are provided throughout the specification.

FIG. 2 is a block diagram illustrating a provider network offering dataprocessing services that utilize other data storage services for tieredstorage, according to some embodiments. Provider network 200 may be aprivate or closed system or may be set up by an entity such as a companyor a public sector organization to provide one or more services (such asvarious types of cloud-based storage) accessible via the Internet and/orother networks to clients 250. Provider network 200 may be implementedin a single location or may include numerous data centers hostingvarious resource pools, such as collections of physical and/orvirtualized computer servers, storage devices, networking equipment andthe like (e.g., computing system 1000 described below with regard toFIG. 10), needed to implement and distribute the infrastructure andstorage services offered by the provider network 200. In someembodiments, provider network 200 may implement various computingresources or services, such as a virtual compute service, dataprocessing service(s) 210, (e.g., map reduce and other large scale dataprocessing services or database services), and data storage services 230(e.g., object storage services or block-based storage services), and/orany other type of network based services (which may include variousother types of storage, processing, analysis, communication, eventhandling, visualization, and security services not illustrated).

In various embodiments, the components illustrated in FIG. 2 may beimplemented directly within computer hardware, as instructions directlyor indirectly executable by computer hardware (e.g., a microprocessor orcomputer system), or using a combination of these techniques. Forexample, the components of FIG. 2 may be implemented by a system thatincludes a number of computing nodes (or simply, nodes), each of whichmay be similar to the computer system embodiment illustrated in FIG. 10and described below. In various embodiments, the functionality of agiven system or service component (e.g., a component of data processingservice 210 or data storage service 220) may be implemented by aparticular node or may be distributed across several nodes. In someembodiments, a given node may implement the functionality of more thanone service system component (e.g., more than one data store component).

Data processing services 210 may be various types of data processingservices to perform different functions (e.g., anomaly detection,machine learning, data mining, querying, or any other type of dataprocessing operation). For example, in at least some embodiments, dataprocessing services 210 may include a map reduce service that createsclusters of processing nodes that implement map reduce functionalityover data stored in one of data storage services 210. In anotherexample, data processing service(s) 210 may include various types ofdatabase storage services (both relational and non-relational) forstoring, querying, and updating data. Such services may beenterprise-class database systems that are highly scalable andextensible. Queries may be directed to a database in data processingservice(s) 210 that is distributed across multiple physical resources,and the database system may be scaled up or down on an as needed basis.The database system may work effectively with database schemas ofvarious types and/or organizations, in different embodiments. In someembodiments, clients/subscribers may submit queries in a number of ways,e.g., interactively via an SQL interface to the database system. Inother embodiments, external applications and programs may submit queriesusing Open Database Connectivity (ODBC) and/or Java DatabaseConnectivity (JDBC) driver interfaces to the database system. Forinstance, data processing services may implement, in some embodiments, adata warehouse service, such as discussed below with regard to FIGS.3-7, that utilizes another data storage service 220 (or a data storeexternal to provider network 200) to implement tiered storage for dataprocessing.

Data storage service(s) 230 may implement different types of data storesfor storing, accessing, and managing data on behalf of clients 250 as anetwork-based service that enables clients 250 to operate a data storagesystem in a cloud or network computing environment. Data storageservice(s) 230 may also include various kinds of object or file datastores for putting, updating, and getting data objects or files. Suchdata storage service(s) 230 may be accessed via programmatic interfaces(e.g., APIs) or graphical user interfaces. Data storage service(s) 230may provide virtual block-based storage for maintaining data as part ofdata volumes that can be mounted or accessed similar to localblock-based storage devices (e.g., hard disk drives, solid state drives,etc.) and may be accessed utilizing block-based data storage protocolsor interfaces, such as internet small computer interface (i SCSI).

Generally speaking, clients 250 may encompass any type of clientconfigurable to submit network-based requests to provider network 200via network 260, including requests for storage services (e.g., arequest to create, read, write, obtain, or modify data in data storageservice(s) 230, etc.). For example, a given client 250 may include asuitable version of a web browser, or may include a plug-in module orother type of code module configured to execute as an extension to orwithin an execution environment provided by a web browser.Alternatively, a client 250 may encompass an application such as adatabase application (or user interface thereof), a media application,an office application or any other application that may make use ofstorage resources in data storage service(s) 230 to store and/or accessthe data to implement various applications. In some embodiments, such anapplication may include sufficient protocol support (e.g., for asuitable version of Hypertext Transfer Protocol (HTTP)) for generatingand processing network-based services requests without necessarilyimplementing full browser support for all types of network-based data.That is, client 250 may be an application configured to interactdirectly with provider network 200. In some embodiments, client 250 maybe configured to generate network-based services requests according to aRepresentational State Transfer (REST)-style network-based servicesarchitecture, a document- or message-based network-based servicesarchitecture, or another suitable network-based services architecture.

In some embodiments, a client 250 may be configured to provide access toprovider network 200 to other applications in a manner that istransparent to those applications. For example, client 250 may beconfigured to integrate with an operating system or file system toprovide storage on one of data storage service(s) 230 (e.g., ablock-based storage service). However, the operating system or filesystem may present a different storage interface to applications, suchas a conventional file system hierarchy of files, directories and/orfolders. In such an embodiment, applications may not need to be modifiedto make use of the storage system service model. Instead, the details ofinterfacing to the data storage service(s) 230 may be coordinated byclient 250 and the operating system or file system on behalf ofapplications executing within the operating system environment.

Clients 250 may convey network-based services requests (e.g., accessrequests to read or write data may be directed to data in data storageservice(s) 230, operations, tasks, or jobs, being performed as part ofdata processing service(s) 220, or to interact with data catalog service210) to and receive responses from provider network 200 via network 260.In various embodiments, network 260 may encompass any suitablecombination of networking hardware and protocols necessary to establishnetwork-based-based communications between clients 250 and providernetwork 200. For example, network 260 may generally encompass thevarious telecommunications networks and service providers thatcollectively implement the Internet. Network 260 may also includeprivate networks such as local area networks (LANs) or wide areanetworks (WANs) as well as public or private wireless networks. Forexample, both a given client 250 and provider network 200 may berespectively provisioned within enterprises having their own internalnetworks. In such an embodiment, network 260 may include the hardware(e.g., modems, routers, switches, load balancers, proxy servers, etc.)and software (e.g., protocol stacks, accounting software,firewall/security software, etc.) necessary to establish a networkinglink between given client 250 and the Internet as well as between theInternet and provider network 200. It is noted that in some embodiments,clients 250 may communicate with provider network 200 using a privatenetwork rather than the public Internet.

In at least some embodiments, one of data processing service(s) 220 maybe a data warehouse service. FIG. 3 is a block diagram of a datawarehouse service that processes data utilizing another data storageservice for tiered storage. A data warehouse service as discussed belowmay offer clients a variety of different data management services,according to their various needs. In some cases, clients may wish tostore and maintain large of amounts data, such as sales recordsmarketing, management reporting, business process management, budgetforecasting, financial reporting, website analytics, or many other typesor kinds of data. A client's use for the data may also affect theconfiguration of the data management system used to store the data. Forinstance, for certain types of data analysis and other operations, suchas those that aggregate large sets of data from small numbers of columnswithin each row, a columnar database table may provide more efficientperformance. In other words, column information from database tables maybe stored into data blocks on disk, rather than storing entire rows ofcolumns in each data block (as in traditional database schemes). Thefollowing discussion describes various embodiments of a relationalcolumnar database system. However, various versions of the componentsdiscussed below as related to storing data in a tree-based data formatmay be equally configured or adapted to implement embodiments forvarious other types of relational database systems, such as row-orienteddatabase systems. Therefore, the following examples are not intended tobe limiting as to various other types or formats of relational databasesystems.

In some embodiments, storing table data in such a columnar fashion mayreduce the overall disk I/O requirements for various queries and mayimprove analytic query performance. For example, storing database tableinformation in a columnar fashion may reduce the number of disk I/Orequests performed when retrieving data into memory to perform databaseoperations as part of processing a query (e.g., when retrieving all ofthe column field values for all of the rows in a table) and may reducethe amount of data that needs to be loaded from disk when processing aquery. Conversely, for a given number of disk requests, more columnfield values for rows may be retrieved than is necessary when processinga query if each data block stored entire table rows. In someembodiments, the disk requirements may be further reduced usingcompression methods that are matched to the columnar storage data type.For example, since each block contains uniform data (i.e., column fieldvalues that are all of the same data type), disk storage and retrievalrequirements may be further reduced by applying a compression methodthat is best suited to the particular column data type. In someembodiments, the savings in space for storing data blocks containingonly field values of a single column on disk may translate into savingsin space when retrieving and then storing that data in system memory(e.g., when analyzing or otherwise processing the retrieved data). Forexample, for database operations that only need to access and/or operateon one or a small number of columns at a time, less memory space may berequired than with traditional row-based storage, since only data blocksstoring data in the particular columns that are actually needed toexecute a query may be retrieved and stored in memory (e.g., onlyretrieving data blocks of those columns from a remote data storeaccording to techniques described below with regard to FIGS. 5-7). Toincrease the efficiency of implementing a columnar relational databasetable, a sort order may be generated and applied so that entries in thedatabase table are stored according to the sort order. When queries arereceived, mapping information, such as may be maintained in a superblockas or other collection of metadata for processing queries may beutilized to locate the data values likely stored in data blocks of thecolumnar relational database table, which may be used to determine datablocks that do not need to be read when responding to a query.

Data warehouse service 300 may be implemented by a large collection ofcomputing devices, such as customized or off-the-shelf computingsystems, servers, or any other combination of computing systems ordevices, such as the various types of systems 1000 described below withregard to FIG. 10. Different subsets of these computing devices may becontrolled by control plane 310. Control plane 310, for example, mayprovide a cluster control interface to clients or users who wish tointeract with the processing clusters 320 managed by control plane 310.For example, control plane 310 may generate one or more graphical userinterfaces (GUIs) for storage clients, which may then be utilized toselect various control functions offered by the control interface forthe processing clusters 320 hosted in the data warehouse service 300.

As discussed above, various clients (or customers, organizations,entities, or users) may wish to store and manage data using a datamanagement service. Processing clusters, such as those discussed belowwith regard to FIG. 4 may respond to various processing requests,including store requests (e.g., to write data into storage) or queriesfor data (e.g., such as a Server Query Language request (SQL) forparticular data), along with many other data management or storageservices. Multiple users or clients may access a processing cluster toobtain data warehouse services. In at least some embodiments, a datawarehouse service may provide network endpoints to the clusters whichallow the clients to send requests and other messages directly to aparticular cluster. Network endpoints, for example may be a particularnetwork address, such as a URL, which points to a particular cluster.For instance, a client may be given the network endpoint“http://mycluster.com” to send various request messages to. Multipleclients (or users of a particular client) may be given a networkendpoint for a particular cluster. Various security features may beimplemented to prevent unauthorized users from accessing the clusters.Conversely, a client may be given network endpoints for multipleclusters.

Processing clusters, such as processing clusters 320 a, 320 b, through320 n, hosted by the data warehouse service 300 may provide anenterprise-class database query and management system that allows usersto send data processing requests to be executed by the clusters 320,such as by sending a data processing request to a cluster controlinterface implemented by the network-based service. Scaling clusters 320may allow users of the network-based service to perform their datawarehouse functions, such as fast querying capabilities over structureddata, integration with various data loading and ETL (extract, transform,and load) tools, client connections with best-in-class businessintelligence (BI) reporting, data mining, and analytics tools, andoptimizations for very fast execution of complex analytic queries suchas those including multi-table joins, sub-queries, and aggregation, moreefficiently.

FIG. 4 is a block diagram illustrating an example processing cluster fora data warehouse service, according to some embodiments. As illustratedin this example, a processing cluster 400 may include a leader node 420and compute nodes 430, 440, and 450, which may communicate with eachother over an interconnect 460. Leader node 420 may generate queryplan(s) 425 for executing queries on processing cluster 400. Asdescribed herein, each node in a processing cluster may include multipledisks on which storage slabs of a table may be stored on behalf ofclients (e.g., users, client applications, and/or storage servicesubscribers). In this example, compute node 430 includes disks 431-438,compute node 440 includes disks 441-448, and compute node 450 includesdisks 451-458. In some embodiments, a component of the processingcluster (or the data warehouse system of which it is a component) maysupport load balancing, using any of a variety of applicable loadbalancing techniques. For example, in some embodiments, leader node 420may include a load balancing component (not shown).

Note that in at least some embodiments, query processing capability maybe separated from compute nodes, and thus in some embodiments,additional components may be implemented for processing queries.Additionally, it may be that in some embodiments, no one node inprocessing cluster 400 is a leader node as illustrated in FIG. 4, butrather different nodes of the nodes in processing cluster 400 may act asa leader node or otherwise direct processing of queries to data storedin processing cluster 400. While nodes of processing cluster may beimplemented on separate systems or devices, in at least someembodiments, some or all of processing cluster may be implemented asseparate virtual nodes or instance on the same underlying hardwaresystem (e.g., on a same server).

In at least some embodiments, processing cluster 400 may be implementedas part of a data warehouse service, as discussed above with regard toFIG. 3, or another data storage service(s) 220, and includes a leadernode 420 and multiple compute nodes, such as compute nodes 430, 440, and450. The leader node 420 may manage communications with clients, such asclients 250 discussed above with regard to FIG. 2. For example, leadernode 420 may be a server that receives requests from various clientprograms (e.g., applications) and/or subscribers (users), then parsesthem and develops an execution plan (e.g., query plan(s)) to carry outthe associated database operation(s)). More specifically, leader node420 may develop the series of steps necessary to obtain results forcomplex queries and joins. Leader node 420 may also manage thecommunications among compute nodes 430 through 450 instructed to carryout database operations for data stored in the processing cluster 400.For example, compiled code may be distributed by leader node 420 tovarious ones of the compute nodes 430 to 450 to carry out the stepsneeded to perform queries, and intermediate results of those queries maybe sent back to the leader node 420. Leader node 420 may receive dataand query responses or results from compute nodes 430, 440, and 450. Adatabase schema and/or other metadata information for the data storedamong the compute nodes, such as the data tables stored in the cluster,may be managed and stored by leader node 420.

Processing cluster 400 may also include compute nodes, such as computenodes 430, 440, and 450. These one or more compute nodes (sometimesreferred to as compute nodes), may for example, be implemented onservers or other computing devices, such as those described below withregard to computer system 1000 in FIG. 10, and each may includeindividual query processing “slices” defined, for example, for each coreof a server's multi-core processor. Compute nodes may perform processingof database operations, such as queries, based on instructions sent tocompute nodes 430, 440, and 450 from leader node 420. The instructionsmay, for example, be compiled code from execution plan segments andsteps that are executable by the particular compute node to which it issent. Compute nodes may send intermediate results from queries back toleader node 420 for final aggregation. Each compute node may beconfigured to access a certain memory and disk space in order to processa portion of the workload for a query (or other database operation) thatis sent to one or more of the compute nodes 430, 440 or 450. Thus,compute node 430, for example, may access disk 431, 432, up until disk438.

Disks, such as the disks 431 through 458 illustrated in FIG. 4, may bemay be implemented as one or more of any type of storage devices and/orstorage system suitable for storing data accessible to the computenodes, including, but not limited to: redundant array of inexpensivedisks (RAID) devices, disk drives (e.g., hard disk drives or solid statedrives) or arrays of disk drives such as Just a Bunch Of Disks (JBOD),(used to refer to disks that are not configured according to RAID),optical storage devices, tape drives, RAM disks, Storage Area Network(SAN), Network Access Storage (NAS), or combinations thereof. In variousembodiments, disks may be formatted to store columnar database tablesthrough various column-oriented database schemes.

In some embodiments, each of the compute nodes in a processing clustermay implement a set of processes running on the node server's (or othercomputing device's) operating system that manage communication with theleader node, e.g., to receive commands, send back data, and routecompiled code to individual query processes (e.g., for each core orslice on the node) in order to execute a given query. In someembodiments, each of compute nodes includes metadata for the blocksstored on the node. In at least some embodiments this block metadata maybe aggregated together into a superblock data structure, which is a datastructure (e.g., an array of data) whose entries store information(e.g., metadata about each of the portions of data (e.g., data blocks)stored on that node (i.e., one entry per data block). In someembodiments, each entry of the superblock data structure includes aunique ID for a respective portion of data (e.g., block), and thatunique ID may be used to perform various operations associated with theportion of data. In at least some embodiments, an entry in thesuperblock may be maintained that indicates the range, such as the minand max values, for sort order values associated with the items storedin the portion of data and described in the superblock.

FIG. 5 is a block diagram illustrating an example compute node thatexecutes processing requests for data in tiered storage, according tosome embodiments. As discussed above, a compute node may be configuredto receive processing requests 512, such as queries, storage operations,and other data management operations. Processing requests 512 may bereceived as inputs to compute node 510. Compute node 510 may communicatewith local storage devices to access locally maintained metadataportion(s) of a data set 542 and locally maintained user data portion(s)544, which may store a plurality of data blocks for multiple columns ofa columnar database table, in order to execute processing requests.

Compute node 510 may also communicate with data storage service 500(which may be a remote data store implemented at another storage servicein the provider network or a different data storage system or serviceexternal to the provider network). If, for instance, additional data(either metadata or user data) is not present in local storage, thencompute node 510 may request specific portions of data 552 from data set540 maintained at data storage service 500. For example, compute node510 may be configured to format a request to generate an API call orISCSI request to retrieve a particular data block, page, chunk or object(or group thereof). Data storage service 500 may return the requesteddata 554 to compute node to be stored as part of locally maintainedmetadata 542 and locally maintained user data 544 (e.g., which may bemaintained at local block-based storage, such as disks 431-458 in FIG.4). In some embodiments, compute node 510 may be configured to sendrequests to update portions of data 556 in data set 540 as part of aflush operation, as discussed below. Compute node 510 may be alsoconfigured to store new or additional data 558 in data set 540.

Compute node 510 may implement data processing engine 520 to executereceived processing requests 512. FIG. 6 is a block diagram illustratingan example execution engine that accesses local and remote data storageto execute processing requests, according to some embodiments. Dataprocessing engine 520 may be configured to parse, interpret, optimize,and execute processing requests 602 (e.g., queries). Data processingengine 520 may implement request execution planning 610 to performvarious operations to optimize execution performance, includingdetermining whether additional data needs to be retrieved from theremote data store. For instance, request execution planning 610 mayaccess locally maintained metadata portions 542 (either in memory or inpersistent storage via local storage interface 630) to evaluate zonemaps (e.g., indicating a range of values stored in a data portion, suchas a data block or chunk), bloom filters (e.g., indicating whethercertain query predicates or conditions are not satisfied by values inthe data portion), or other probabilistic data structures (e.g.,generated by hyperloglog), which may be implemented to prune or identifyportions of the data set which do not need to be read in order tosatisfy a processing request. Additionally metadata may describe thecontents of locally maintained user data, as well as the contents of thedata set as a whole. Metadata may be retrieved from data storage service500 via remote storage interface 640 in order to obtain more descriptivemetadata for optimize a processing request execution, in someembodiments (e.g., obtaining additional probabilistic data structures,more fine grained zone maps (data block level, as opposed to a zone mapfor a group of data blocks). Like the user data, metadata that isfrequently accessed may be maintained locally for quick access.

If request execution planning identifies data that needs to be obtainedfrom data storage service 500, then request execution planning mayinclude the identity of the additional data in instructions provided torequest execution 620 in order to obtain the data from data storageservice 500 via remote storage interface 640. Request execution 620 mayalso update metadata (e.g., probabilistic data structures) as a resultof executing a processing request.

Compute node 510 may implement local data management 530 to manage thecontents of local storage maintaining metadata and user data. FIG. 7 isa block diagram illustrating local data management that maintains localdata storage for executing processing requests, according to someembodiments. Local data management 530 may implement statistics trackingsuch as data portion access tracking 710 to determine how frequentlydata portions are accessed (e.g., how often are individual data blocksread to execute a processing request). Data portion access tracking 720may, in various embodiments, implement the statistics determinationsdiscussed below with regard to FIG. 9, for example by maintainingaccount of the number of times a portion of data is accessed, as well asa time since the data portion was last accessed. In this way, techniquesthat balance frequency and recency can be implemented to select portionsof the data set to keep or evict. Other statics or information formaking evictions decisions can be determined by data portion accesstracking 710 (e.g., by maintaining a queue or representation of whendata portions were placed into local storage in order to implement aFIFO eviction scheme). Some data portion eviction techniques may beimplemented based on predictive analysis of data portion accessstatistics. For example, statistical analyses (e.g., linear regression,pattern recognition, frequency analysis, etc.) may be implemented topredict the usage of portions of the data set (either user data ormetadata that will or will not be used (e.g., at a future point orperiod in time). Based on the predicted usage, data portion eviction 720may select those portions of locally maintained portion(s) of data set542 or metadata 544 to flush. For instance, portions not likely to beaccessed may be identified based on predicted usage and thus may beflushed from local storage.

Local data management 530 may implement data portion eviction 720 tocopy or update portions via local storage interface 730 being flushedfrom locally maintained metadata 542 or user data 544 to be stored atdata storage service 500 via remote storage interface 740.

Note that the various arrangements of components illustrated in FIGS.5-7 are provided as an example of a compute node that may utilizedtiered storage. In other embodiments, different combinations ofcomponents (e.g., combining portions or all of local data management 530within portions of data processing engine 520 may be implemented andthus the previous discussions and illustrations is not intended to belimiting as to other implementations of a compute node that utilizestiered storage.

Although FIGS. 2-7 have been described and illustrated in the context ofa data storage service, like a data warehousing system implementing acolumnar relational database table, the various components illustratedand described in FIGS. 2-7 may be easily applied to other datamanagement systems that provide data processing on behalf of clients. Assuch, FIGS. 2-6 are not intended to be limiting as to other embodimentsof a processing cluster. FIG. 8 is a high-level flowchart illustratingmethods and techniques to execute processing requests accessing tieredstorage, according to some embodiments.

Various different systems and devices may implement the various methodsand techniques described below, either singly or working together. Forexample, a processing cluster, such as described above with regard toFIGS. 3-7 may be configured to implement the various methods.Alternatively, a combination of different systems and devices.Therefore, the above examples and or any other systems or devicesreferenced as performing the illustrated method, are not intended to belimiting as to other different components, modules, systems, orconfigurations of systems and devices.

As indicated at 810, portion(s) of a data set that is also maintained ata remote data store may be maintained at a data processor. For example,previously executed processing requests may be received, portions of thedata set to be utilized for data processing the processing requests maybe identified, and the identified portions may be obtained, as discussedbelow. In some embodiments, the access statistics, such as thosecollected for portions of a data set, discussed below with regard toFIG. 9, may be utilized to pre-load or obtain portions of the data setlikely to be accessed. For example, tracking statistics may indicatethat a group of data blocks in the data set is most frequently accessedand thus these data blocks may obtained and stored in local storage atthe data processor prior to the execution of processing requests. Inanother example, predicted usage of data (determined according tovarious statistical analyses of access statistics discussed above) maybe used to identify which portions of the data set to obtain andmaintain in local storage without or before receiving a processingrequest that requires the data for execution. In some embodiments,portions of the data set may be initially identified, obtained, andstored according to an assignment scheme (e.g., a hash-baseddistribution).

As indicated at 820, a processing request direct to the data set may bereceived at the data processor, in some embodiments. For example, aquery formatted according to SQL or a custom processing task, job, orother work assignment formatted according to a programmatic interfacemay be received via a network-based interface from a client of the dataprocessor (e.g., a database client). The request may specify variousconditions or criteria for execution that identify which portions of thedata set may be needed to execute the processing request (e.g., querypredicates). These conditions or criteria may be evaluated with respectto the locally stored portions of the data (e.g., by accessing localmetadata and user data). If different or additional portions are neededto execute the query that are not maintained locally at the dataprocessor, as indicated by the positive exit from 830, then thedifferent portion(s) may be obtained by the data processor from theremote data store, as indicated at 840. For instance, an API request toget one or more data blocks, chunks, objects, or pages may be sent, oran iSCSI request to retrieve, the different portion(s) may be sent tothe remote data store which may implement the API or iSCSI interface andreturn the requested portion(s).

Once the requested portion(s) are obtained, then as indicated at 850,the processing request may be executed by the data processor based, atleast in part, on the obtained portion(s) from the remote data store.For instance, if the obtained portions are user data, then the user datamay be accessed or evaluated with respect to the conditions or criteriafor executing the request. In another example, if the obtained portionsare metadata describing user data in the data set, then the metadata maybe evaluated to locate data portions of user data to evaluate, prune I/Ooperations, or perform other optimizations to the execution of theprocessing request. If different or additional portion(s) are not neededat the data processor to process the processing request, then asindicated by the negative exit from 830, the data processor may executethe processing request based on the data that is already maintainedlocally at the data processor, as indicated at 860.

FIG. 9 is a high-level flowchart illustrating methods and techniquesflush portions of a data set locally maintained at a data processor to aremote data store, according to some embodiments. As indicated at 910,access statistics may be tracked for portions of the data set locallymaintained at the data processor. In some embodiments, an access countfor each portion may be incremented each time the portion is accessed.The time since the last access may also be maintained, in someembodiments. A timestamp may be recorded at the last access. Asindicated at 920 a decay function may be applied to the accessstatistics according to a time since the last access for the portion(s)to determine weighted access statistics for the portion(s), in variousembodiments. For example, the decay function may reduce the count foreach portion by a factor or number determine based on the amount orlength of the time since the last access of the portion. For example, ifa data portion was last accessed 1 day ago, then the decay function mayreduce the count for the portion by ⅕, whereas if the last access for aportion were 1 week ago, then the count may be reduced by ½. In thisway, data that is frequently accessed at one point time does not remainin the local storage if the accesses do not subsequently continue,preventing a rare access scenario (e.g., an infrequent processingrequest) from dominating the local storage capacity of more recently(but less frequently) accessed portions.

As indicated at 930, one of the portion(s) may be selected for removalfrom local storage based, at least in part, on the weighted accessstatistics. For instance, a count threshold may be implemented thatremoves portions with counts below the threshold. In some embodiments, aranking selection may be implemented to choose a lowest ranked number ofportions according to the weighted access statistics. Once selected, theportion may be flushed from local storage to update the correspondingportion in a data set maintained at the remote data store, as indicatedat 940. For example, the selected portion may be used to overwrite thecorresponding portion in the remote data store (so that the value(s) ofthe portion are now stored in the remote data store). Then the selectedportion may be reclaimed, marked for deletion or as available to beoverwritten with a new portion of data obtained from the remote datastore (as discussed above with regard to FIG. 8).

The methods described herein may in various embodiments be implementedby any combination of hardware and software. For example, in oneembodiment, the methods may be implemented by a computer system (e.g., acomputer system as in FIG. 10) that includes one or more processorsexecuting program instructions stored on a computer-readable storagemedium coupled to the processors. The program instructions may beconfigured to implement the functionality described herein (e.g., thefunctionality of various servers and other components that implement thenetwork-based virtual computing resource provider described herein). Thevarious methods as illustrated in the figures and described hereinrepresent example embodiments of methods. The order of any method may bechanged, and various elements may be added, reordered, combined,omitted, modified, etc.

Embodiments of tiered storage for data processing as described hereinmay be executed on one or more computer systems, which may interact withvarious other devices. One such computer system is illustrated by FIG.10. In different embodiments, computer system 1000 may be any of varioustypes of devices, including, but not limited to, a personal computersystem, desktop computer, laptop, notebook, or netbook computer,mainframe computer system, handheld computer, workstation, networkcomputer, a camera, a set top box, a mobile device, a consumer device,video game console, handheld video game device, application server,storage device, a peripheral device such as a switch, modem, router, orin general any type of computing or electronic device.

In the illustrated embodiment, computer system 1000 includes one or moreprocessors 1010 coupled to a system memory 1020 via an input/output(I/O) interface 1030. Computer system 1000 further includes a networkinterface 1040 coupled to I/O interface 1030, and one or moreinput/output devices 1050, such as cursor control device 1060, keyboard1070, and display(s) 1080. Display(s) 1080 may include standard computermonitor(s) and/or other display systems, technologies or devices. In atleast some implementations, the input/output devices 1050 may alsoinclude a touch- or multi-touch enabled device such as a pad or tabletvia which a user enters input via a stylus-type device and/or one ormore digits. In some embodiments, it is contemplated that embodimentsmay be implemented using a single instance of computer system 1000,while in other embodiments multiple such systems, or multiple nodesmaking up computer system 1000, may be configured to host differentportions or instances of embodiments. For example, in one embodimentsome elements may be implemented via one or more nodes of computersystem 1000 that are distinct from those nodes implementing otherelements.

In various embodiments, computer system 1000 may be a uniprocessorsystem including one processor 1010, or a multiprocessor systemincluding several processors 1010 (e.g., two, four, eight, or anothersuitable number). Processors 1010 may be any suitable processor capableof executing instructions. For example, in various embodiments,processors 1010 may be general-purpose or embedded processorsimplementing any of a variety of instruction set architectures (ISAs),such as the x86, PowerPC, SPARC, or MIPS ISAs, or any other suitableISA. In multiprocessor systems, each of processors 1010 may commonly,but not necessarily, implement the same ISA.

In some embodiments, at least one processor 1010 may be a graphicsprocessing unit. A graphics processing unit or GPU may be considered adedicated graphics-rendering device for a personal computer,workstation, game console or other computing or electronic device.Modern GPUs may be very efficient at manipulating and displayingcomputer graphics, and their highly parallel structure may make themmore effective than typical CPUs for a range of complex graphicalalgorithms. For example, a graphics processor may implement a number ofgraphics primitive operations in a way that makes executing them muchfaster than drawing directly to the screen with a host centralprocessing unit (CPU). In various embodiments, graphics rendering may,at least in part, be implemented by program instructions configured forexecution on one of, or parallel execution on two or more of, such GPUs.The GPU(s) may implement one or more application programmer interfaces(APIs) that permit programmers to invoke the functionality of theGPU(s). Suitable GPUs may be commercially available from vendors such asNVIDIA Corporation, ATI Technologies (AMD), and others.

System memory 1020 may be configured to store program instructionsand/or data accessible by processor 1010. In various embodiments, systemmemory 1020 may be implemented using any suitable memory technology,such as static random access memory (SRAM), synchronous dynamic RAM(SDRAM), nonvolatile/Flash-type memory, or any other type of memory. Inthe illustrated embodiment, program instructions and data implementingdesired functions, such as those described above are shown stored withinsystem memory 1020 as program instructions 1025 and data storage 1035,respectively. In other embodiments, program instructions and/or data maybe received, sent or stored upon different types of computer-accessiblemedia or on similar media separate from system memory 1020 or computersystem 1000. Generally speaking, a non-transitory, computer-readablestorage medium may include storage media or memory media such asmagnetic or optical media, e.g., disk or CD/DVD-ROM coupled to computersystem 1000 via I/O interface 1030. Program instructions and data storedvia a computer-readable medium may be transmitted by transmission mediaor signals such as electrical, electromagnetic, or digital signals,which may be conveyed via a communication medium such as a networkand/or a wireless link, such as may be implemented via network interface1040.

In one embodiment, I/O interface 1030 may be configured to coordinateI/O traffic between processor 1010, system memory 1020, and anyperipheral devices in the device, including network interface 1040 orother peripheral interfaces, such as input/output devices 1050. In someembodiments, I/O interface 1030 may perform any necessary protocol,timing or other data transformations to convert data signals from onecomponent (e.g., system memory 1020) into a format suitable for use byanother component (e.g., processor 1010). In some embodiments, I/Ointerface 1030 may include support for devices attached through varioustypes of peripheral buses, such as a variant of the Peripheral ComponentInterconnect (PCI) bus standard or the Universal Serial Bus (USB)standard, for example. In some embodiments, the function of I/Ointerface 1030 may be split into two or more separate components, suchas a north bridge and a south bridge, for example. In addition, in someembodiments some or all of the functionality of I/O interface 1030, suchas an interface to system memory 1020, may be incorporated directly intoprocessor 1010.

Network interface 1040 may be configured to allow data to be exchangedbetween computer system 1000 and other devices attached to a network,such as other computer systems, or between nodes of computer system1000. In various embodiments, network interface 1040 may supportcommunication via wired or wireless general data networks, such as anysuitable type of Ethernet network, for example; viatelecommunications/telephony networks such as analog voice networks ordigital fiber communications networks; via storage area networks such asFibre Channel SANs, or via any other suitable type of network and/orprotocol.

Input/output devices 1050 may, in some embodiments, include one or moredisplay terminals, keyboards, keypads, touchpads, scanning devices,voice or optical recognition devices, or any other devices suitable forentering or retrieving data by one or more computer system 1000.Multiple input/output devices 1050 may be present in computer system1000 or may be distributed on various nodes of computer system 1000. Insome embodiments, similar input/output devices may be separate fromcomputer system 1000 and may interact with one or more nodes of computersystem 1000 through a wired or wireless connection, such as over networkinterface 1040.

As shown in FIG. 10, memory 1020 may include program instructions 1025,configured to implement the various methods and techniques as describedherein, and data storage 1035, comprising various data accessible byprogram instructions 1025. In one embodiment, program instructions 1025may include software elements of embodiments as described herein and asillustrated in the Figures. Data storage 1035 may include data that maybe used in embodiments. In other embodiments, other or differentsoftware elements and data may be included.

Those skilled in the art will appreciate that computer system 1000 ismerely illustrative and is not intended to limit the scope of thetechniques as described herein. In particular, the computer system anddevices may include any combination of hardware or software that canperform the indicated functions, including a computer, personal computersystem, desktop computer, laptop, notebook, or netbook computer,mainframe computer system, handheld computer, workstation, networkcomputer, a camera, a set top box, a mobile device, network device,internet appliance, PDA, wireless phones, pagers, a consumer device,video game console, handheld video game device, application server,storage device, a peripheral device such as a switch, modem, router, orin general any type of computing or electronic device. Computer system1000 may also be connected to other devices that are not illustrated, orinstead may operate as a stand-alone system. In addition, thefunctionality provided by the illustrated components may in someembodiments be combined in fewer components or distributed in additionalcomponents. Similarly, in some embodiments, the functionality of some ofthe illustrated components may not be provided and/or other additionalfunctionality may be available.

Those skilled in the art will also appreciate that, while various itemsare illustrated as being stored in memory or on storage while beingused, these items or portions of them may be transferred between memoryand other storage devices for purposes of memory management and dataintegrity. Alternatively, in other embodiments some or all of thesoftware components may execute in memory on another device andcommunicate with the illustrated computer system via inter-computercommunication. Some or all of the system components or data structuresmay also be stored (e.g., as instructions or structured data) on acomputer-accessible medium or a portable article to be read by anappropriate drive, various examples of which are described above. Insome embodiments, instructions stored on a non-transitory,computer-accessible medium separate from computer system 1000 may betransmitted to computer system 1000 via transmission media or signalssuch as electrical, electromagnetic, or digital signals, conveyed via acommunication medium such as a network and/or a wireless link. Variousembodiments may further include receiving, sending or storinginstructions and/or data implemented in accordance with the foregoingdescription upon a computer-accessible medium. Accordingly, the presentinvention may be practiced with other computer system configurations.

It is noted that any of the distributed system embodiments describedherein, or any of their components, may be implemented as one or moreweb services. For example, leader nodes within a data warehouse systemmay present data storage services and/or database services to clients asnetwork-based services. In some embodiments, a network-based service maybe implemented by a software and/or hardware system designed to supportinteroperable machine-to-machine interaction over a network. Anetwork-based service may have an interface described in amachine-processable format, such as the Web Services DescriptionLanguage (WSDL). Other systems may interact with the web service in amanner prescribed by the description of the network-based service'sinterface. For example, the network-based service may define variousoperations that other systems may invoke, and may define a particularapplication programming interface (API) to which other systems may beexpected to conform when requesting the various operations.

In various embodiments, a network-based service may be requested orinvoked through the use of a message that includes parameters and/ordata associated with the network-based services request. Such a messagemay be formatted according to a particular markup language such asExtensible Markup Language (XML), and/or may be encapsulated using aprotocol such as Simple Object Access Protocol (SOAP). To perform a webservices request, a network-based services client may assemble a messageincluding the request and convey the message to an addressable endpoint(e.g., a Uniform Resource Locator (URL)) corresponding to the webservice, using an Internet-based application layer transfer protocolsuch as Hypertext Transfer Protocol (HTTP).

In some embodiments, web services may be implemented usingRepresentational State Transfer (“RESTful”) techniques rather thanmessage-based techniques. For example, a web service implementedaccording to a RESTful technique may be invoked through parametersincluded within an HTTP method such as PUT, GET, or DELETE, rather thanencapsulated within a SOAP message.

The various methods as illustrated in the FIGS. and described hereinrepresent example embodiments of methods. The methods may be implementedin software, hardware, or a combination thereof. The order of method maybe changed, and various elements may be added, reordered, combined,omitted, modified, etc.

Various modifications and changes may be made as would be obvious to aperson skilled in the art having the benefit of this disclosure. It isintended that the invention embrace all such modifications and changesand, accordingly, the above description to be regarded in anillustrative rather than a restrictive sense.

What is claimed is:
 1. A system, comprising: one or more storagedevices, configured to maintain at least some of a data set additionallymaintained at a remote data store; one or more compute nodes, comprisingat least one processor and a memory, configured to implement aprocessing engine for executing processing requests directed to the dataset; the processing engine, configured to: receive a processing requestdirected to the data set; in response to the receipt of the processingrequest: identify a portion of the data set not maintained at the one ormore storage devices in order to execute the processing request; send arequest to the remote data store to obtain the portion of the data setfrom the remote data store; upon receipt of the portion of the data set,execute the processing request based, at least in part, on the portionof the data set obtained from the remote data store.
 2. The system ofclaim 1, wherein the one or more compute nodes are further configured toimplement a local data manager, configured to: track access statisticsfor different portions of the at least some data maintained in thestorage devices; apply a decay function to the access statisticsaccording to respective amounts of time since the different portionswere last accessed to determine weighted access statistics for thedifferent portions; select one of the different portions for removalfrom the persistent storage devices based, at least in part, on theweighted access statistics; and flush the selected portion from thepersistent storage devices to update a corresponding portion in the dataset maintained in the remote data store.
 3. The system of claim 1,wherein the processing engine is further configured to: receive anotherprocessing request directed to the data set; in response to the receiptof the other request: identify the at least some data maintained in thestorage devices for execution of the other request without additionaldata from the remote data store; and execute the other processingrequest based, at least in part, on the at least some data of the dataset.
 4. The system of claim 1, wherein the data processor is aprocessing cluster implemented as part of a data warehouse service in aprovider network, wherein the remote data store is implemented as partof a different service in the provider network.
 5. A method, comprising:performing, by one or more computing devices: receiving a processingrequest directed to a data set at a data processor that executesprocessing requests directed to the data set, wherein the data set ismaintained at a data store remote to the data processor, wherein thedata processor maintains one or more portions of the data set at thedata processor for executing processing requests; in response toreceiving the processing request: identifying, by the data processor, adifferent portion of the data set not maintained at the data processorfor executing the processing request; obtaining, by the data processor,the different portion of the data set from the remote data store; andexecuting, by the data processor, the processing request based, at leastin part, on the different portion of the data set.
 6. The method ofclaim 5, wherein the obtained portion of the data set is metadatadescribing one or more portions of user data in the data set and whereinexecuting the processing request comprises optimizing execution of theprocessing request according to the obtained metadata.
 7. The method ofclaim 6, wherein the obtained metadata further describes the one or moreportions of the user data in addition to other metadata maintained atthe data processor that also describes the one or more portions of theuser data.
 8. The method of claim 5, further comprising: selecting, bythe data processor, one of the portions or the different portion of thedata set to remove from local storage at the data processor; andflushing, by the data processor, the selected portion from the localstorage at the data processor to update a corresponding portion in thedata set maintained in the remote data store.
 9. The method of claim 8,further comprising: tracking, by the data processor, access statisticsfor the portions and the different portion of the data set maintained atthe data processor; and applying, by the data processor, one or morestatistical analyses to the access statistics to determine usagepredictions for the portions and the different portion of the data set,wherein the selection of the one portion is based, at least in part, onthe usage predictions.
 10. The method of claim 5, wherein the processingrequest is query comprising one or more predicates, wherein identifyingthe different portion of the data set not maintained at the dataprocessor for executing the processing request comprises evaluatingmetadata describing the data set maintained at the data processor thatindicates those portions of the data set that satisfy at least one ofthe query predicates.
 11. The method of claim 5, further comprising:prior to receiving the processing request: identifying, by the dataprocessor, the one or more portions of the data set based, at least inpart, on access statistics tracked for the one or more portions; andobtaining, by the data processor, the one or more portions of the dataset from the remote data store.
 12. The method of claim 5, furthercomprising: receiving another processing request directed to the dataset at the data processor; in response to receiving the other request:identifying, by the data processor, the one or more portions of the dataset maintained at the data processor for execution of the other requestwithout additional data from the remote data store; and executing, bythe data processor, the other processing request based, at least inpart, on the one or more portions of the data set.
 13. The method ofclaim 5, wherein the data processor is implemented as part of anetwork-based service of a provider network, and wherein the remote datastore is another network-based service of the provider network or a datastore external to the provider network.
 14. A non-transitory,computer-readable storage medium, storing program instructions that whenexecuted by one or more computing devices cause the one or morecomputing devices to implement: maintaining, at a data processer, atleast some of a data set that is also maintained at a remote data store;receiving, at the data processor, a request directed to the data set; inresponse to receiving the processing request: identifying, by the dataprocessor, a different portion of the data set not maintained at thedata processor for executing the processing request; obtaining, by thedata processor, the different portion of the data set from the remotedata store; and executing, by the data processor, the processing requestbased, at least in part, on the different portion of the data set. 15.The non-transitory, computer-readable storage medium of claim 14,wherein the program instructions cause the one or more computing devicesto further implement: receiving another processing request directed tothe data set at the data processor; in response to receiving the otherrequest: identifying, by the data processor, the one or more portions ofthe data set maintained at the data processor for execution of the otherrequest without additional data from the remote data store; andexecuting, by the data processor, the other processing request based, atleast in part, on the one or more portions of the data set.
 16. Thenon-transitory, computer-readable storage medium of claim 14, whereinthe processing request is a query comprising one or more querypredicates, wherein the identified portion is user data identified aspossibly containing data that satisfies at least one of the querypredicates, and wherein executing the processing request comprisesevaluating the identified portion with respect to the query predicates.17. The non-transitory, computer-readable storage medium of claim 16,wherein the data set is maintained as a table in a column-orientedformat, wherein the obtained portion of the data set comprises a portionof a column entries in the table.
 18. The non-transitory,computer-readable storage medium of claim 14, wherein the programinstructions cause the one or more computing devices to furtherimplement: selecting, by the data processor, one of the portions or thedifferent portion of the data set to remove from local storage at thedata processor; and flushing, by the data processor, the selectedportion from the local storage at the data processor to update acorresponding portion in the data set maintained in the remote datastore.
 19. The non-transitory, computer-readable storage medium of claim18, wherein the programming instructions cause the one or more computingdevices to further implement: tracking, by the data processor, accessstatistics for the portions and the different portion of the data setmaintained at the data processor; and applying, by the data processor, adecay function to the access statistics according to respective amountsof time since the portions and the different portion of the data setwere last accessed to determine weighted access statistics for theportions and the different portion of the data set, wherein theselection of the one portion is based, at least in part, on the weightedaccess statistics.
 20. The non-transitory, computer-readable storagemedium of claim 14, wherein the remote data store is implemented as partof a network-based service of a provider network, and wherein the dataprocessor is implemented as part of another network-based service of theprovider network or a data processor external to the provider network.